Department of Computational Intelligence, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland.
Department of Electronics, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Kraków, Poland.
PLoS One. 2019 Oct 29;14(10):e0224194. doi: 10.1371/journal.pone.0224194. eCollection 2019.
In the world, in which acceptance and the identification with social communities are highly desired, the ability to predict the evolution of groups over time appears to be a vital but very complex research problem. Therefore, we propose a new, adaptable, generic, and multistage method for Group Evolution Prediction (GEP) in complex networks, that facilitates reasoning about the future states of the recently discovered groups. The precise GEP modularity enabled us to carry out extensive and versatile empirical studies on many real-world complex / social networks to analyze the impact of numerous setups and parameters like time window type and size, group detection method, evolution chain length, prediction models, etc. Additionally, many new predictive features reflecting the group state at a given time have been identified and tested. Some other research problems like enriching learning evolution chains with external data have been analyzed as well.
在这个渴望被社会群体接纳和认同的世界里，预测群体随时间的演变的能力似乎是一个至关重要但非常复杂的研究问题。因此，我们提出了一种新的、适应性强的、通用的、多阶段的复杂网络群体演化预测（GEP）方法，便于推理最近发现的群体的未来状态。精确的 GEP 模块化使我们能够在许多真实世界的复杂/社交网络上进行广泛而多样的实证研究，以分析大量设置和参数（如时间窗口类型和大小、群体检测方法、演化链长度、预测模型等）的影响。此外，还确定和测试了许多反映给定时间内群体状态的新预测特征。还分析了其他一些研究问题，如用外部数据丰富学习演化链。
